The most important things cannot be measured. The issues that are most important, long term, cannot be measured in advance.

—W. Edwards Deming

Working software is the primary measure of progress.

—Agile Manifesto

 

Metrics

Metrics are agreed-upon measures used to evaluate how well the organization is progressing toward the portfolio, large solution, program, and team’s business and technical objectives.

Thanks to its work physics, Kanban systems, timeboxes, and fast feedback, Agile is inherently more measurable than its proxy-based predecessor, the waterfall process. Moreover, with Agile, the “system always runs.” So, the best measure comes directly from the objective evaluation of the working system. Continuous delivery and DevOps practices provide even more things to measure. All other measures—even the extensive set of Lean-Agile metrics outlined below—are secondary to the overriding goal of focusing on rapid delivery of high-quality solutions.

But metrics are indeed important in the enterprise context. SAFe provides metrics for each level of the Framework. The links below navigate to the entries on this page.

Portfolio Metrics

Lean Portfolio Metrics

In the spirit of ‘the simplest set of measures that can work,’ Figure 1 provides a comprehensive set of Lean portfolio metrics that can be used to assess internal and external progress for an entire portfolio.

Figure 1. Lean portfolio metrics

Lean Portfolio Management Self-Assessment

The Lean Portfolio Management (LPM) continuously assesses and improves their methods. The LPM periodically conducts a self-assessment questionnaire to measure their performance, which automatically produces a radar chart like the one shown in Figure 2. It highlights the relative strengths and weaknesses.

 

Figure 2. Portfolio self-assessment radar chart

Download Portfolio Self-Assessment

Large Solution Metrics

Solution Train Predictability Measure

The Agile Release Trains (ARTs) predictability measures are summarized to calculate the Solution Train’s predictability measure, as illustrated in Figure 3.

 

Figure 3. Solution Train predictability measure

Solution Train Performance Metrics

The ARTs performance metrics are summarized to calculate the Solution Train’s performance metrics, as shown in Figure 4.

 

Figure 4. Solution Train performance metrics

Program Metrics

Feature Progress Report

The feature progress report tracks the status of features and enablers during PI execution. It indicates which features are on track or behind at any point in time. The chart has two bars:

  • Plan – Represents the total number of stories planned.
  • Actual – Represents the number of stories completed. The bar is shaded red or green, depending on whether or not the item is on track.

Figure 5 gives an example of a feature progress report.

 

Figure 5. Feature progress report, highlighting the status of each Feature compared to the Program Increment plan

Program Predictability Measure

The team PI performance reports are summarized to determine the program predictability measure, as illustrated in Figure 6. The report compares actual business value achieved to planned business value.

Figure 6. Program predictability measure, showing two of the teams on the train and program (cumulative)

For more on this approach, see chapter 15 of Agile Software Requirements: Lean Requirements Practices for Teams, Programs, and the Enterprise [1].

Program Performance Metrics

The end of each PI is a natural and significant measuring point. Figure 7 shows a set of performance metrics for a program.

Figure 7. One train’s chart of performance metrics

Cumulative Flow Diagram

The Cumulative Flow Diagram (CFD) is made up of a series of lines or areas that show the amount of work in different Kanban states. For example, the typical states of the program Kanban are:

  • Funnel
  • Analyzing
  • Backlog
  • Implementing
  • Validating on staging
  • Deploying to production
  • Releasing
  • Done

Figure 8 shows the number of features in each Kanban state by day. The thicker areas in the CFD represent potential bottlenecks.

 

Figure 8. Example program kanban CFD

Agile Release Train Self-Assessment

As program execution is a core value of SAFe, the ART continuously strives to improve its performance. The Release Train Engineer (RTE) fills out the self-assessment questionnaire at PI boundaries or any time the train wants to pause and reflect on their progress. Trending this data over time is a key performance indicator. Figure 9 gives an example of the results in a radar chart.

 

Figure 9. Agile Release Train self-assessment radar chart

Download Agile Release Train Assessment

Continuous Delivery Pipeline Efficiency

The pipeline efficiency compares the amount of touch time versus wait time. Some of the information can be sourced automatically from tools, especially Continuous Integration, and Continuous Deployment, while other data requires manually recording in a spreadsheet. The value stream mapping technique is often applied to analyze problems identified in this report.

Note: The touch time represents when the team is adding value. Typically, touch time is only a small proportion of the total production time, most of the time is spent waiting, such as when moving work, waiting in queues and so on. This is clearly shown in Figure 10.

 

Figure 10. Continuous Delivery Pipeline efficiency

Deployments and Releases per Timebox

This metric is meant to demonstrate whether the program is making progress toward deploying and releasing more frequently. It can be viewed on a PI basis, as shown in Figure 11.

 

Figure 11. Deployments and releases per Program Increment

Or we can zoom in to see how releases are handled in mid-PI, as shown in Figure 12.

 

Figure 12. Deployments and releases per Iteration

Recovery over Time

This report measures the number of rollbacks that occurred either physically or by turning off feature toggles. The date when a solution was deployed or released to production is also plotted here to determine if there is a relationship between the two (Figure 13).

Figure 13. Recovery over time

 

Innovation Accounting and Leading Indicators

One of the goals of the Continuous Delivery Pipeline is to enable the organization to run experiments quickly to allow Customers to validate the hypotheses. As a result, both Minimal Marketable Features (MMFs) and Minimal Viable Products (MVPs) must define the leading indicators to measure progress toward the benefit hypothesis. Avoid relying on vanity metrics that do not measure real progress.

Figure 14 shows some metrics that were gathered from the SAFe website to demonstrate leading indicators for our development efforts.

Figure 14. Leading indicators for SAFe website innovation accounting

DevOps Health Radar

The DevOps Health Radar is a tool to assess the progress of your program in improving the flow of value through Continuous Delivery Pipeline. Figure 15 shows the 16 sub-dimensions that programs should use to assess their maturity. It helps to identify the sub-dimensions in which we are sitting, crawling, walking, running, or flying, and identify places to improve.

Figure 15. DevOps Health Radar

Download DevOps Health Radar

You can also go to our partner’s site, Agile Transformation, to take an online version of the assessment at https://agilityhealthradar.com/safe-devops-assessment.

Team Metrics

Iteration Metrics

Each Agile Team gathers the iteration metrics they’ve agreed to collect. This occurs in the quantitative part of the team retrospective. Figure 16 illustrates the chart for the measurements of one team.

 

Figure 16. One team’s chart of iteration metrics

Team PI Performance Report

During the PI System Demo, the Business Owners, Customers, Agile teams, and other key stakeholders rate the actual business value (BV) achieved for each team’s PI Objectives as shown in Figure 17.

 

Figure 17. Team PI performance report

Reliable trains should operate in the 80 – 100 percent range; this allows the business and its outside stakeholders to plan effectively. Below are some notes about how the report works:

  • The planned total BV does not include uncommitted objectives to help the reliability of the train
  • The actual total BV does include uncommitted objectives
  • The achievement percentage is the actual BV ÷ planned BV
  • A team can achieve greater than 100 percent (as a result of uncommitted objectives achieved)

Individual team totals are rolled up into the program predictability measure (see Figure 17).

SAFe Team Self-Assessment

Agile teams continuously assess and improve their process. One such tool is a simple SAFe team practices assessment. When the team completes the spreadsheet, it will automatically produce a radar chart like the one shown in Figure 18, which highlights relative strengths and weaknesses.

Figure 18. SAFe Team self-assessment radar chart

Download Agile Team self-assessment

 

Learn More

[1] Leffingwell, Dean. Agile Software Requirements: Lean Requirements Practices for Teams, Programs, and the Enterprise. Addison-Wesley, 2011.

[2] Leffingwell, Dean. Scaling Software Agility: Best Practices for Large Enterprises. Addison-Wesley, 2007.

[3] https://www.youtube.com/watch?v=VOjPpeBh40s

Last update: 19 December 2019

 

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